Parametric images are commonly used for graphically representing the result of quantitative analyses in diagnostic applications. Particularly, this technique may be used for the assessment of blood perfusion in contrast-enhanced ultrasound imaging. For this purpose, an ultrasound contrast agent (UCA)—for example, consisting of a suspension of phospholipid-stabilized gas-filled microbubbles—is administered to a patient. The contrast agent acts as an efficient ultrasound reflector, and can be easily detected by applying ultrasound waves and measuring the echo signals that are returned in response thereto. Since the contrast agent flows at the same velocity as red-blood cells in the patient, its detection and tracking provides information about blood perfusion in a body-part under analysis. Particularly, the echo signal that is recorded over time for each location of the body-part is associated with a mathematical model function; the model function is used to calculate any desired perfusion parameter (for example, a wash-in rate), which characterizes the location of the body-part. A parametric image is then generated by assigning, to each pixel representing a location of the body-part, the corresponding value of the perfusion parameter (in brief, “perfusion parameter value”). The parametric image shows the spatial distribution of the perfusion parameter values throughout the body-part, so as to facilitate the identification of possible locations thereof that are abnormally perfused (for example, because of a pathological condition).
The parametric images may also be used to perform statistical analysis based on histograms. For example, “Histogram Analysis versus Region of Interest Analysis of Dynamic Susceptibility Contrast Perfusion MR Imaging Data in the Grading of Cerebral Gliomas, M. Law et al., AJNR Am J Neuroradiol 28:761-66, April 2007”, which is incorporated by reference, describes the use of this technique in contrast-enhanced Magnetic Resonant (MR) imaging applications. Particularly, a Cerebral Blood Volume (CBV) map is created (being limited between minimum and maximum values required to maintain appropriate color scales). The CBV map is then normalized to a value of unaffected tissue (typically, normal contralateral white matter). A histogram of the values in a Region of Interest (ROI) of the CBV map is now calculated. This histogram is used to assess a grade of a corresponding glioma—for example, based on its standard deviation or on multiple metrics (being identified by means of a binary logistic regression).
Likewise, “Glioma Grading by Using Histogram Analysis of Blood Volume Heterogeneity from MR-derived Cerebral Blood Volume Maps, Kyrre E. Emblem et al., Radiology: Volume 247: Number 3—June 2008, pages 808-817”, which is incorporated by reference, describes the calculation of a histogram from a normalized CBV map; a resulting curve is then normalized to the value of one. Glioma malignancy can be assessed by determining a peak height of the histogram distribution (with the result that can be further improved with analysis of the histogram shape).
Moreover, “Histogram Analysis of MR Imaging-Derived Cerebral Blood Volume Maps: Combined Glioma Grading and Identification of Low-Grade Oligodendroglial Subtypes, K. E. Emblem et al., AJNR Am J Neuroradiol 29:1664-70, October 2008”, which is incorporated by reference, describes the same technique with the use of a cutoff value for the peak height, in order to identify glioma grades and low-grades oligodendroglial subtypes (even if the authors themselves recognize that the definition of the cutoff value is difficult in practice, so that its transferability is inherently reduced).
As a last example, “Assessing tumour response to treatment: Histogram analysis of parametric maps of tumour vascular function derived from dynamic contrast-enhanced MR images, C. Hayes et al., Proceedings of ISMRM 2000, Denver, Colo., USA, April 2000”, which is incorporated by reference, describes the use of statistical analysis of parametric images in contrast-enhanced MR applications (for example, based on permeability) to assess tumor response to treatment. Particularly, this document proposes the use of values of median, range, or skewness (as illustrated qualitatively by the corresponding histograms).
However, when applied to the case of contrast-enhanced ultrasound imaging, the above-described statistical analyses produce results that strongly depend on the equipments that are used to record the echo signals (from which the parametric image is generated); moreover, even when using a given equipment, different results are obtained by varying its settings (for example, gain, log-compression, and so on). Therefore, these results are not suitable for an absolute quantitative evaluation. Moreover, the results cannot be compared among investigators using different equipments or settings.